151: Lateral thoughts #7 - the problems with wind power

Fig. 151.1: Wind turbines.

There are many claims that are made about wind power, not least that it is cheap. It isn't. In fact it costs almost the same as nuclear.

A nuclear power plant costs about £10bn and delivers 1GW of power almost constantly over a lifetime of up to fifty years. So that is £10 of capital cost per watt of output.

A 1MW wind turbine costs about £1.25m (offshore turbines cost even more). So that is only £1.25 of capital cost per watt of nominal output, much less than nuclear. But wind turbines rarely deliver their maximum or nominal output because they cannot operate in high winds for safety reasons, and at normal wind speeds (v) the output varies as v³. So a drop in wind speed of 50% results in the output power dropping to an eighth of its previous value (see Fig. 151.2 below). 

 

Fig. 151.2: The observed power output of a 1.5MW wind turbine.

 

But there is another problem, and that is that wind speeds are weighted in their frequency of occurrence towards lower values (see Fig. 151.3 below). The result is the power output is both highly variable and weighted towards low values, and so most turbines struggle to deliver more than 33% on average over time of their nominal output. So the true capital cost of a wind turbine is about £3.75 per watt of output. But if we also factor in the 25 year lifetime of wind turbines (i.e. half that of nuclear), then the true capital cost relative to nuclear is going to be about £7.50 per watt. So wind is only marginally cheaper, and remember, offshore wind is even more expensive.

 

Fig. 151.3: The observed frequency of wind speed.

But this is not the biggest problem with wind power. That is the energy storage or backup dilemma. What do we do when the wind doesn't blow?

This was the problem in December of last year. The UK experienced a cold snap with temperatures dropping below -10°C. This is not unusual: it happens every year and is caused by an area of high pressure sitting over the UK. So, just as the UK needed more power for extra heating in the cold weather in mid-December, there was no power coming from the UK's main renewable source: wind power. But this is not just a winter problem. A similar phenomenon is seen during heatwaves in summer. In both cases the wind across most of the UK drops to almost zero for days, or sometimes even weeks on end. So how do we compensate for this?

Well, there are two options. We can either build extra wind turbines to generate surplus electricity in times of plenty and store the excess energy, or we can build backup generators using different and more reliable energy sources.

The problem with the energy storage route is the sheer amount of storage required. The cold snap described above lasted about a week but could have lasted up to twenty days. According to Worldometers, the UK generated 318,157 GWh of electricity in 2016, or about 870 GWh per day. That is 3132 TJ per day, or the energy equivalent of exploding fifty Hiroshima-sized atomic bombs every day. So twenty days of storage would require the equivalent energy storage of over one thousand atomic bombs. And if we want to completely de-carbonize our energy and transport systems that number could easily double. That would require an awful lot of batteries and so is totally unrealistic. It cannot be done.

So what about backup alternatives? Well the issues here are cost and reliability. Because wind is unreliable the backup source needs to be very reliable and immediately accessible. But it also needs to be green. So the obvious candidate is nuclear. But nuclear is more expensive than wind power, so using it as a backup means adding its capital cost to that of wind power when it is rarely going to be used. That makes no sense economically. If we are going to build enough nuclear power stations to satisfy all our electricity needs when the wind isn't blowing, then we may as well use them continuously all the time rather than keeping them idle as backups. If a backup is only going to be used sporadically then its capital cost needs to be much smaller than that of the primary generator it is backing up otherwise it is just an unnecessary additional cost. That leaves only two viable options for backup energy sources: coal and natural gas.

Coal and gas powered generators are up to ten times cheaper than the equivalent-sized nuclear station or wind farm, so their capital costs are negligible in comparison. They are also reliable, but they are not green. That said, they would only be used intermittently so their carbon emissions would be low.

So here is the dilemma. If we stick with wind power then we will need to compromise and allow some fossil fuels to be used as backup supplies in times of need. This will still massively reduce our CO₂ emissions but it will not make us carbon neutral. The only alternative is to abandon wind power and go nuclear.

144: Evidence against temperature adjustments #4 (British Isles)

In the previous four posts I examined the temperature changes for Ireland (see Post 140), Scotland (see Post 142), England (see Post 143) and Great Britain (see Post 141). While all four sets of temperature data appeared similar from 1900 onwards, there were some differences, and these differences were most apparent in a comparison of the earlier data for Ireland and Great Britain. When the Great Britain data was separated into different trends for Scotland and England a similar degree of difference was observed with the Scotland data appearing to correlate more closely with Ireland, and England with Great Britain. In this post I will look to show this pictorially by comparing the various trends directly.

First, if we compare the data for Ireland, Scotland and England with Great Britain we see that England shows the closest agreement after 1900 but Scotland shows the better agreement before 1840 (see Fig. 144.1 below). The data depicted here are the 5-year moving averages of the mean temperature anomalies (MTAs) for each country as shown by the yellow curves in Fig. 140.2, Fig. 141.2, Fig. 142.2 and Fig. 143.2 in previous posts.

Fig. 144.1: The 5-year average temperature trends since 1760 for Ireland, Scotland and England each compared to that of Great Britain. For clarity the trends for Ireland and England are offset by +2°C and -1.5°C respectively.

What is striking about the trends in Fig. 144.1 is how similar they all are after 1860, while the greatest disparities occur before 1860. The reason for this is evident from Fig. 144.2 below which shows that the number of stations used to calculate each of the MTA for Ireland, Scotland and England drops below five before 1870. From this we can conclude two things. First, this suggests that if there are too few stations used in determining the MTA the accuracy decreases. Secondly we see that when there are sufficient stations used to determine the MTA the accuracy is so good that there is little difference between the MTA for different neighbouring countries. 

This is not the first time such conclusions have been drawn. The same effects were seen in Post 138 (Evidence against temperature adjustments #3) comparing trends in the different Scandinavian countries and Post 57 (The case against temperature data adjustments #1) comparing them in various central European countries. In all cases the conclusion is the same. If trends for neighbouring countries agree, then they are likely to all be correct, not all equally incorrect. Therefore no adjustments to the temperature data are needed or justified. A similar result is also encountered when comparing random samples of stations from the same region as was shown for the USA in Post 67 (More evidence against temperature data adjustments #2). The reason for this is that averaging a sufficiently large number of independent data sets results in a reduction in the size of the errors imported from each. This is known as regression towards the mean.

Fig. 144.2: The number of station records included each month in the averaging for the mean temperature trends in Fig. 144.1.

The second comparison I have performed is to compare data for Ireland, Scotland and England with each other. This is shown in Fig. 144.3 below. Now we see that the two countries that agree most closely are Scotland and Ireland while the data for England appears to exhibit more warming after 1980 and before 1900. This additional warming could be in excess of 0.5°C since 1840.

Fig. 144.3: Comparisons of the 5-year average temperature trends since 1760 for England and Scotland (two top curves, both offset by +2°C), Scotland and Ireland (two middle curves), and Ireland and England (two bottom curves, both offset by -2°C).

Conclusions

Once again a comparison of temperature data for neighbouring countries indicates that most adjustments to the data are unnecessary as the averaging process will correct for most errors via regression towards the mean.

The data for Scotland and Ireland are in closest agreement, probably because both have similar population densities and are more rural.

The data for England is in closest agreement with that of Great Britain, probably because England is the largest country in Great Britain and so its stations will always make the dominant contribution compared to other countries such as Scotland or Wales. 

The greater warming seen in England (of over 0.5°C) is further evidence that warming within countries is driven not just by carbon dioxide levels in the atmosphere and the greenhouse effect, but by local energy consumption as well. So net-zero will not be a panacea.

143: England - temperature trends WARMING

It is probably not surprising that England has more weather stations of note than Scotland. After all it has about ten times the population and almost twice the area. Yet the difference is not as great as one might imagine. For while Scotland has nine long stations with over 1200 months of data before 2014, England has only a slight advantage with ten stations. For medium stations with over 480 months of data the difference is greater with England having 55 compared to 13 in Scotland. There is, however, more clustering of stations in England as the map in Fig. 143.1 below shows.

Fig. 143.1: The (approximate) locations of the 65 longest weather station records in England. Those stations with a high warming trend between 1911 and 2010 are marked in red while those with a cooling or stable trend are marked in blue. Those denoted with squares are long stations with over 1200 months of data, while diamonds denote medium stations with more than 480 months of data.

In order to quantify the changes to the climate of England the temperature anomalies for all stations with over 480 months of data before 2014 were determined and averaged. This was done using the usual method as outlined in Post 47 and involved first calculating the temperature anomaly each month for each station relative to its monthly reference temperature (MRT), and then averaging those anomalies to determine the mean temperature anomaly (MTA) for the whole country for each month. The MRTs for England were calculated using the same 30-year period as for the UK in Post 141, namely from 1956-1985. 

The resulting MTA is shown as a time series in Fig. 143.2 below and clearly shows that temperatures rose slightly over 150 years up until 1975 before increasing more rapidly thereafter. In this respect the MTA data for England more resembles that of Great Britain (see Fig. 141.2 in Post 141) than it does that of Scotland (see Fig. 142.2 in Post 142) or Ireland (see Fig. 140.2 in Post 140).

Fig. 143.2: The mean temperature change for England since 1760 relative to the 1956-1985 monthly averages. The best fit is applied to the monthly mean data from 1826 to 1975 and has a positive gradient of +0.35 ± 0.08 °C per century.

The temperature trend for England was calculated using the usual method as outlined in Post 47 and involved first calculating the temperature anomaly each month for each station relative to its monthly reference temperature (MRT), and then averaging those anomalies to determine the mean temperature anomaly (MTA) for the whole country for each month. The graph in Fig. 143.3 below indicates how many stations were available each month in order to contribute to that month's MTA.

The MRTs for England were calculated using the same 30-year period as for the UK in Post 141, namely from 1956-1985. The resulting MTA is shown as a time series in Fig. 143.2 above and clearly shows that temperatures were slowly increasing for over 150 years up until 1975. Then at some point in the 1980s (probably in 1988) the mean temperature appears to increase abruptly by about 1°C. This is a phenomenon that has been seen in many other temperature trends across Europe.

Fig. 143.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for England in Fig. 143.2.

If we next consider the change in temperature based on Berkeley Earth (BE) adjusted data we get the MTA data in Fig. 143.4 below. This again was determined by averaging each month the anomalies from the 65 longest stations and also suggests that the climate was warming slowly before 1980 but then warmed more strongly by over 1°C thereafter.

Fig. 143.4: Temperature trends for England based on Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1826-1975 and has a positive gradient of +0.27 ± 0.03°C/century.

The difference between the MTA based on raw unadjusted data (from Fig. 143.2) and the MTA based on BE adjusted data (from Fig. 143.4) is shown in Fig. 143.5 below. The blue curve in Fig. 143.5 is the difference in MTA values between the adjusted data (Fig. 143.4) and the unadjusted data (Fig. 143.2) and represents the total of all the data adjustments made including those from homogenization, gridding, Kriging and most significantly breakpoint adjustments. The orange curve is the contribution to those adjustments arising solely from breakpoint adjustments.

Fig. 143.5: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 143.4 after smoothing with a 12-month moving average. The blue curve represents the total BE adjustments including those from homogenization. The linear best fit (red line) to these adjustments for the period 1871-2010 has a small positive gradient of +0.003 ± 0.003 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

The overall impact of any adjustments can perhaps be seen more clearly if we compare the 5-year averages for the raw unadjusted data and the BE adjusted data as is shown in Fig. 143.6 below. This shows that the two datasets agree almost perfectly from 1870 onwards while before 1870 the adjusted data implies the climate is more stable. It should be noted though that the MTA trends before 1870 are based on data from less than five stations and this drops to less than two before 1850 (see Fig. 143.3). This is one reason why the MTA exhibits more natural variability over the earlier period before 1870.

Fig. 143.6: The 5-year mean temperature change for England since 1760 based on the original raw data from Fig. 143.2 (in blue) and the Berkeley Earth adjusted data from Fig. 143.4 (in red).

Summary

What the raw data for England shows is that the climate was warming slowly for most of the period up to 1975 (see Fig. 143.2). This is similar to the trend seen previously for Great Britain (see Fig. 141.2 in Post 141), but is different from both Scotland (see Fig. 142.2 in Post 142) and Ireland (see Fig. 140.2 in Post 140) where little warming was seen in this period. This suggests that England is the dominant country in determining the overall climate of the UK but is also the outlier. But why?

The obvious answer is that England has a much greater population density and so experiences much more urban or surface heating from human activities (see Post 14, Post 29, Post 127 and Post 134). As I pointed out in Section (iv) of Post 127, the energy consumption of Greater London is sufficient to raise the local temperature by over 4°C.


Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

Long station = a station with over 1200 months (100 years) of data before 2014.

Medium station = a station with over 480 months (40 years) of data before 2014.

142: Scotland - temperature trends STABLE before 1980

In my previous post I looked at the temperature trends for Great Britain, i.e. the United Kingdom (UK) minus Northern Ireland. These exhibited a large amount of warming (over 1°C), most of which has occurred after 1980. This is not surprising as it is in agreement with other temperature trends that I have analysed, most of which also appear to exhibit some warming after 1980. However, in Great Britain there was still significant warming before 1980, albeit at a much slower rate compared to the post-1980 period. This is more unusual and is also slightly different to the situation found in Ireland (see Post 140) where any warming before 1980 was negligible. So why the difference? Is Ireland the outlier, or is it Great Britain? 

One way to find out is to look separately at the constituent parts of Great Britain: England, Scotland and Wales. If some of these are more similar to Ireland, then that may suggest Ireland is not the outlier but some other parts of the UK may be. Unfortunately there are only about eight stations in Wales of any note, of which only five are medium stations with over 480 months of data, and none have more than a thousand months of data. This means that it is only possible to determine an accurate temperature trend for Wales since 1970. As the most significant differences in the temperature data of Ireland and Great Britain occur well before 1970, the data from Wales is unlikely to be of much use is determining the cause. So for this analysis I will concentrate on England and first Scotland where the quality of the data is far greater.

Fig. 142.1: The (approximate) locations of the 22 longest weather station records in Scotland. Those stations with a high warming trend between 1911 and 2010 are marked in red while those with a cooling or stable trend are marked in blue. Those denoted with squares are long stations with over 1200 months of data, while diamonds denote medium stations with more than 480 months of data.

Scotland has nine long stations with over 1200 months of data before 2014 and a further thirteen medium stations with over 480 months of data. These stations are well distributed across the region as the map in Fig. 142.1 above illustrates. This means a simple average of their monthly temperature anomalies should yield a reasonably accurate temperature trend for the country as a whole. This trend is shown in Fig. 142.2 below.

Fig. 142.2: The mean temperature change for Scotland since 1760 relative to the 1956-1985 monthly averages. The best fit is applied to the monthly mean data from 1826 to 1975 and has a slight positive gradient of +0.18 ± 0.07 °C per century.

In order to quantify the changes to the climate of Scotland the temperature anomalies for all stations with over 480 months of data before 2014 were determined and averaged. This was done using the usual method as outlined in Post 47 and involved first calculating the temperature anomaly each month for each station relative to its monthly reference temperature (MRT), and then averaging those anomalies to determine the mean temperature anomaly (MTA) for the whole country for each month. The MRTs for Scotland were calculated using the same 30-year period as for the UK in Post 141, namely from 1956-1985. The resulting MTA is shown as a time series in Fig. 142.2 and clearly shows that temperatures were fairly stable for over 150 years up until 1975 with only a slight increase being detectable. However, this increase is less than the natural variation in the 5-year average (see the yellow curve in Fig142.2).

Then at some point in the 1980s (probably in 1988) the mean temperature appears to increase abruptly by about 1°C. This is a phenomenon that has been seen in many other temperature trends across Europe. There is also some evidence of additional warming before 1840 which results in an average trend of +0.28°C per century from 1781 to 1980, a 50% increase on the trend for 1826-1975 in Fig. 142.2. However, as the trend before 1850 is based on data from only two stations (see Fig. 142.3 below) it cannot be relied upon.

Fig. 142.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for Scotland in Fig. 142.2.

If we next consider the change in temperature based on Berkeley Earth (BE) adjusted data we get the MTA data in Fig. 142.4 below. This again was determined by averaging each month the anomalies from the 22 longest stations and suggests that the climate was fairly stable before 1880 but then warmed by over 1°C thereafter. In fact the 10-year average suggests there was no warming from 1781 to 1920 but the trend from 1901 to 2020 shows a warming of over 0.75°C. Not only that but the warming is more continuous in nature than the raw data in Fig. 142.2 indicates.

Fig. 142.4: Temperature trends for Scotland based on Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1826-1975 and has a positive gradient of +0.33 ± 0.03°C/century.

What is also apparent is that the trend in Fig. 142.4 for data from 1826 to 1975 is almost double the equivalent trend in Fig. 142.2. The reason for this is the adjustments made to the data by Berkeley Earth (BE). These adjustments include homogenization, gridding, Kriging and most significantly breakpoint adjustments. These lead to changes to the original temperature data, the magnitude of these adjustments being the difference in the MTA values seen in Fig. 142.4 and the raw data in Fig. 142.2. The magnitudes of these adjustments are shown graphically in Fig. 142.5 below. 

Fig. 142.5: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 142.4 after smoothing with a 12-month moving average. The blue curve represents the total BE adjustments including those from homogenization. The linear best fit (red line) to these adjustments for the period 1851-2010 has a positive gradient of +0.158 ± 0.002 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

The blue curve in Fig. 142.5 is the difference in MTA values between the adjusted data (Fig. 142.4) and the unadjusted data (Fig. 142.2), while the orange curve is the contribution to those adjustments arising solely from breakpoint adjustments. Overall these adjustments appear to add almost 0.3°C of warming to the trend between 1840 and 2010. Before 1840 the adjustments reduce the warming. The overall impact can be seen more clearly if we compare the 5-year averages for the raw data and the BE adjusted data as is shown in Fig. 142.6 below.

Fig. 142.6: The 5-year mean temperature change for Scotland since 1760 based on the original raw data from Fig. 142.2 (in blue) and the Berkeley Earth adjusted data from Fig. 142.4 (in red).

What the data in Fig. 142.6 shows is the amount of warming that has been added by the BE adjustments. While it is less than the natural warming it is still significant and adds over 0.2°C of warming to the period from 1876 to 2010. The result is a trend of 0.74°C per century after 1875 (as shown in Fig. 142.7 below) compared to only 0.57°C per century for the raw data in Fig. 142.2 for the same period. The main impact of the adjustments before 1900 appears to be to flatten the curve and thus eliminate any

Fig. 142.7: Temperature trends for Scotland based on Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1876-2010 and has a positive gradient of +0.74 ± 0.03°C/century.

Summary

What the raw data for Scotland shows is that the climate was stable for 150 years up to 1975 with warming of less than 0.18°C per century. This is similar to that seen in Ireland of 0.14°C per century (see Fig. 140.2 in Post 140) and significantly less than the value of 0.46°C per century for Great Britain (see Fig. 141.2 in Post 141). This suggests that Ireland and Scotland are not the outliers. So is England, and why?


Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

Long station = a station with over 1200 months (100 years) of data before 2014.

Medium station = a station with over 480 months (40 years) of data before 2014.

140: Ireland - temperature trends STABLE before 1980

The island of Ireland has nineteen weather stations with over 480 months of data before the end of 2013. All but two of these are in the Republic of Ireland. The two stations in Northern Ireland (Armagh and Belfast Airport) are though both long stations with over 1200 months of data. In addition there are a further six long stations in the Republic of Ireland together with eleven medium stations (for a full list see here). The locations of these nineteen stations are shown on the map in Fig. 140.1 below. Other than a small cluster around Dublin, the stations are evenly spread across the island. This means that any average of the temperature anomalies from these nineteen stations should approximate well to the true relative temperature change for Ireland. What this averaging shows is that the climate of Ireland was fairly stable until 1980 but with medium term fluctuations of up to 1°C in the mean temperature. After 1980 the mean temperature has probably risen by about 1°C. In other words, the rise since 1980 is comparable to the fluctuations.

Fig. 140.1: The (approximate) locations of the 19 longest weather station records in Ireland. Those stations with a high warming trend are marked in red while those with a cooling or stable trend are marked in blue. Those denoted with squares are long stations with over 1200 months of data, while diamonds denote medium stations with more than 480 months of data.

 

In order to quantify the changes to the climate of Ireland the temperature anomalies for all stations with over 480 months of data before 2014 were determined and averaged. This was done using the usual method as outlined in Post 47 and involved first calculating the temperature anomaly each month for each station, and then averaging those anomalies to determine the mean temperature anomaly (MTA) for the region. This MTA is shown as a time series in Fig. 140.2 and clearly shows that temperatures were fairly stable up until 1980. However at some point in the 1980s (probably in 1988) the mean temperature appears to increase abruptly by almost 1°C.

Fig. 140.2: The mean temperature change for Ireland since 1820 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1846 to 1975 and has a positive gradient of +0.14 ± 0.08 °C per century.

The process of determining the MTA in Fig. 140.2 involved first determining the monthly reference temperatures (MRTs) for each station using a common reference period, in this case from 1961 to 1990, and then subtracting the MRTs from the raw temperature data to deliver the anomalies. If a station had at least twelve valid temperatures per month within the MRT interval then its anomalies were included in the calculation of the mean temperature anomaly (MTA). The total number of stations included in the MTA in Fig. 140.2 each month is indicated in Fig. 140.3 below. The peak in the frequency between 1960 and 2010 suggests that the 1961-1990 interval for the MRTs was a good choice.

Fig. 140.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for Ireland in Fig. 140.2.

If we next consider the change in temperature based on Berkeley Earth (BE) adjusted data we get the MTA data in Fig. 140.4 below. This again was determined by averaging each monthly anomaly from the nineteen longest stations and suggests that the climate was fairly stable before 1920 but then warmed thereafter. In fact the 10-year average suggests a warming of almost 1.5°C from 1840 to 2000. Not only that but the warming is more continuous in nature than the raw data in Fig. 140.2 actually shows.

Fig. 140.4: Temperature trends for Ireland based on Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1896-2005 and has a positive gradient of +0.63 ± 0.04°C/century.

If we compare the curves in Fig. 140.4 with the published Berkeley Earth (BE) version for Ireland in Fig. 140.5 below we see that there is good agreement between the two sets of data. This indicates that the simple averaging of anomalies used to generate the BE MTA in Fig. 140.4 using adjusted data is as effective and accurate as the more complex gridding method used by Berkeley Earth in Fig. 140.5. In which case simple averaging should be just as effective and accurate in generating the MTA using raw unadjusted data in Fig. 140.2.

Fig. 140.5: The temperature trend for Ireland since 1750 according to Berkeley Earth.

Any differences between the MTA based on raw unadjusted data in Fig. 140.2 and the BE version using adjusted data in Fig. 140.4 are mainly due to the data processing procedures used by Berkeley Earth. These include homogenization, gridding, Kriging and most significantly breakpoint adjustments. These lead to changes to the original temperature data, the magnitude of these adjustments being the difference in the MTA values seen in Fig. 140.2 and Fig. 140.4. 

Fig. 140.6: The contribution of Berkeley Earth (BE) adjustments to the anomaly data in Fig. 140.4 after smoothing with a 12-month moving average. The blue curve represents the total BE adjustments including those from homogenization. The linear best fit (red line) to these adjustments for the period 1881-2010 has a positive gradient of +0.028 ± 0.003 °C per century. The orange curve shows the contribution just from breakpoint adjustments.

The magnitudes of these adjustments are shown graphically in Fig. 140.6 above. The blue curve is the difference in MTA values between adjusted (Fig. 140.4) and unadjusted data (Fig. 140.2), while the orange curve is the contribution to those adjustments arising solely from breakpoint adjustments. The overall adjustment from 1880 to 1990 is small, less than ±0.1°C. The largest adjustments to the data occur after 1990 and before 1880. These adjustments add almost 0.2°C of warming to the data after 1990 and add almost 0.3°C of cooling to the data before 1880. While these changes are small in themselves, cumulatively they add almost 0.5°C to the warming trend. The full impact of these adjustments can be seen most clearly by comparing the the 5-year moving averages of the data in Fig. 140.2 and Fig. 140.4 as shown in Fig. 140.7 below.

Fig. 140.7: The 5-year mean temperature change for Ireland since 1820 based on the original raw data from Fig. 140.2 (in blue) and the Berkeley Earth adjusted data from Fig. 140.4 (in red).

Summary

According to the raw unadjusted temperature data, the climate of Ireland remained stable for 150 years up until the 1980s (see Fig. 140.2). Then it suddenly increased in temperature by almost 1°C. Why?

In contrast, adjusted temperature data from Berkeley Earth claims to show that the climate of Ireland has warmed more or less continuously since 1900. This warming is a bit more than 1°C (see Fig. 140.4).

Comparing the adjusted MTA data (see Fig. 140.4) with the unadjusted MTA data (see Fig. 140.2) suggests that the adjustments may have added up to 0.5°C to the overall warming since 1850 (see Fig. 140.7).


Acronyms

BE = Berkeley Earth.

MRT = monthly reference temperature (see Post 47).

MTA = mean temperature anomaly.

List of all stations in the Republic of Ireland with links to their raw data files.

14. Surface heating

The principal claim made by climate scientists is that global temperatures have increased by about 1 °C over the last 100 years. In the last post I outlined three ways that this might happen. The first, which was due to changes in the amount of solar radiation reaching the Earth, I discounted due to a lack of evidence or plausible mechanism. The last, changes to the radiative forcing term I will discuss at a later date. In this post I will consider the second possibility: changes to the amount of direct heat absorption at the surface of the Earth. There are essentially only two ways this can happen: (i) through changes to the Earth’s reflectivity or albedo; (ii) by direct heating of the surface from energy sources other than the Sun.

(i) Changing the Earth’s albedo.

As I explained in the last post, one way that the Earth's surface temperature might change is if the proportion of light from the Sun that is reflected from the surface were to change. The amount reflected is called the albedo. This effect can be seen in Fig. 14.1 below which is taken from a 2009 paper by Kevin Trenberth, John Fasullo and Jeffrey Kiehl (Bull. Amer. Meteor. Soc. 90 (3): 311–324). On the left of Fig. 14.1 where the direct radiation from the Sun (in yellow) impacts the surface, the radiation is partially reflected with 23 W/m² being reflected and 161 W/m² is absorbed. This equates to an albedo of 0.125 ( = 23/(23+161) ).

As an aside: it seems slightly suspicious that the fractions reflected at the surface (1/8) and at the top of the atmosphere (102/341 = 30%) are so close to simple fractions. Does this indicate a high degree of uncertainty in these numbers, I wonder?

Fig. 14.1: The Earth's energy balance according to Trenberth et al. (2009). 

In order for the surface temperature of the Earth to have increased by 1 °C, one way that this could have happened would be for the amount of energy absorbed at the surface to have increased over time by 2.3 W/m². If this were to be achieved through changes to the albedo, then the albedo would need to have decreased from 0.1375 to 0.125. That is a change of 0.0125. So how likely is this?

The albedo of the Earth's surface depends of the type of material of the surface, as shown in Table 14.1. It also depends on the angle of incidence of the light as light tends to reflect more off surfaces at glazing incidence. So ocean water at the equator has a lower albedo than it does near the poles. However, there is also much less surface area near the poles which consequently reduces the contribution of high angle reflectance. 

Surface	% of Earth's     Surface Area	Albedo    %	Contribution to the     Earth's Albedo
Ocean	71.00	6	0.0426
Forest	7.62	8-18	0.0091
Grassland	7.93	25	0.0198
Arable	2.37	17	0.0040
Desert sand	5.51	40	0.0220
Urban	0.21	20	0.0004
Glaciers & ice caps	2.90	80	0.0232
Shrub & tundra	2.46	15	0.0037

Table 14.1: Approximate albedo of different parts of the Earth's surface.

The most common claims made about land use and climate changes are in regard to deforestation, increasing agricultural use, and increased urbanization. First it is claimed that deforestation for farming, particularly livestock farming aids global warming. As far as changes to the albedo are concerned, the evidence in Table 14.1 seems to point the other way. Turning forests into grassland increases the albedo.

Urbanization is also generally believed to reduce albedo, partly through what is termed the urban heat island (UHI) effect. This is the theory that cities with large amounts of concrete soak up more heat, and tall buildings trap that heat. This may be true, but it may also be a small localized effect. Again the data in Table 14.1 does not support it as a major driver of global warming.

A third claim is often made about polar ice and glaciers. The claim is that, because ice and snow have high levels of albedo, any change in their total albedo would have a large impact on global temperatures. The two main negative effects cited tend to be reductions in area by melting, or black carbon soot particles that drop on the surface and reduce the albedo. The main problem here is that the changes required are huge; a 54% decrease in area, or a decrease in albedo from 0.80 to 0.37. The first obviously has not and will not happen, and the latter is very unlikely as it would require huge levels of soot deposits.

The conclusion is, therefore, that changes to the Earth's albedo are difficult to achieve, and any that might have occurred have probably produced very little real effect in terms of increasing global temperatures.



(ii) Direct anthropogenic surface heating due to human and industrial activity.

The proposition here is this. All energy generation by humans results in an output of heat or thermal energy. Not only does every industrial process produce waste heat, but all mechanical work that is done by that process eventually ends up as heat or entropy as well. These are the consequences of the Second Law of Thermodynamics, and as every physicist knows, nothing can defeat the Second Law of Thermodynamics. So as temperature is just a measure of heat and entropy, it follows that everything humans do, every industrial process they create, all the energy that goes in will, in the end, just heat up the environment.

In the last post I showed that an increase of 2.3 ± 0.5 W/m² in the amount of radiation at the surface would raise global temperatures by 1 °C. So if we can work out what the rate of energy production and consumption by humans is, then we can equate that to a global temperature rise. The starting point for this is clear: we know from IPCC reports and the protestations of climate scientists that the human race currently emits 36 gigatonnes of carbon dioxide (CO₂) into the atmosphere. That CO₂ is created primarily by three processes.

The first is the burning of pure carbon (from coal) that produces an energy output of 394 kJ/mol for the process

(14.1)

The second is burning of methane (natural gas) that produces an energy output of 882 kJ/mol for the process

(14.2)

The third is the burning of higher alkanes (from oil) that produces an energy output of about 660 kJ per mole of CO₂ for the process

(14.3)

Each of the above energy outputs is for the burning of carbon or hydrocarbons to produce one mole of CO₂. To work out how energy that amounts to in total we need to know how much of each type of fossil fuel was used.

In 2018 global coal production was 7665 million tonnes, natural gas production was 3955 billion cubic metres or 2786 million tonnes (assuming 1 cubic metre = 704.5 g), and crude oil production was 4472 million tonnes. That suggests a mean energy output of about 560 kJ/mol. As 36 gigatonnes of carbon dioxide equates to 8.18 x 10m¹⁴ moles, then the total energy consumption would have been 4.58 x 10²⁰ J for the year, or 52,268 TWh.

Fig. 14.2: Global fossil fuel consumption since 1800.

However, according to the Our World In Data website, the global energy consumption from fossil fuels in 2017 amounted to 36,704 TWh from natural gas, 53,752 TWh from crude oil and 43,397 TWh from coal (see Fig. 14.2 above). The total of these values (133,853 TWh) is 2.53 times the value based on CO₂ emissions and suggests only 39% of fossil fuel combustion results in CO₂. This higher figure equates to an average power density at the Earth's surface of 0.030 W/m² across the whole surface of the Earth. That is turn implies a global temperature increase (based on the 2.3 W/m² required for a 1 °C increase that I demonstrated in the last post) of 0.013 °C compared to pre-industrial times. This, though, still omits the impact of nuclear power and renewables.

Fig. 14.3: Global energy production by energy type (2005-2018).

According to Statistica.com renewables and nuclear energy accounted for 15.3% of global energy consumption in 2018, and fossil fuel usage in 2018 exceeded that in 2017 (see Fig. 14.3 above), so that implies a global temperature increase of at least 0.015 °C compared to pre-industrial times. This temperature increase of 0.015 °C is, however, at least 60 times less than the one the IPCC is claiming for global warming since 1850. So this suggests that any resulting surface heating is such a small effect that we can safely ignore it, right? Well, not so fast.

We know that this heat is not spread evenly, its impact is greatest in the areas where most people live and work. We know that 90% of people live in the Northern Hemisphere; we know that 99.999% of people live on land. It is also true that 90% of weather stations are in the Northern Hemisphere, and at least 99.9% of them are on land. In other words there is a high degree of correlation between where people live, where industrial energy usage is, and where the weather stations are. For example, 19.7% of the Earth's surface is land in the Northern Hemisphere. So if 90% of the energy use is found there then the mean temperature rise on land in the Northern Hemisphere will be 0.069 °C. But of course, even that fails to tell the whole story. If we look at individual countries the results become even more stark.

If we start with what has been, historically, the biggest CO₂ producer, the USA, we see that it accounts for about 20% of global energy use despite being home to only 4.3% of the world's population, and covering only 1.6% of the Earth's surface area. That suggests that the power density for surface heating in the USA should be about 0.38 W/m² (an increase by a factor of 12.6 on the global average of 0.03 W/m²). This picture is confirmed by data from the US Energy Information Administration that indicates that the total power consumption of the 48 contiguous states (excluding Hawaii and Alaska) is 100.3 x 10¹⁵ BTU (see Fig. 14.4 below) over an area of 8.08 x 10⁶ km². As 1 BTU (British thermal unit) is the equivalent of 1055 J, this gives a power density for surface heating of 0.42 W/m². Yet this increases to 0.69 W/m² in Texas and 1.11 W/m² in Pennsylvania. That means that the temperature rise in Pennsylvania due to surface heating is almost 0.5 °C. But if we look at Europe the situation is even more extreme.

 Fig. 14.4: US energy consumption since 1950 by sector (in BTU).

According to the IEA, the UK's energy usage in 2018 was 177 million tonnes of oil equivalent (Mtoe), or 2059 TWh (1 Mtoe = 11.63 MWh). As the area of the UK is only 242,495 km², that equates to a power density of 0.97 W/m² and a temperature rise of 0.42 °C. But it is safe to assume that that energy usage will not be spread evenly across the country. At least 84% of both the UK population and UK economic activity is found in England (with an area of 130,395 km²) which implies a temperature rise for England alone of 0.66 °C. Yet that is still modest compared to Belgium and the Netherlands with their much higher population densities (see Table 14.2 below) where the projected temperature rise is close to 1.0 °C. That is more than the IPCC claims for global warming from greenhouse gas emissions.

Country	Energy Usage (Mtoe)	Power Density (W/m2)	Temperature Rise (°C)
UK	177	0.97	0.42
Italy	151	0.66	0.29
France	245	0.50	0.22
Belgium	52	2.25	0.98
Netherlands	72	2.30	1.00
Germany	298	1.11	0.48
Austria	33	0.52	0.23
Switzerland	24	0.77	0.34

Table 14.2: Energy usage, surface heating and temperature rise in Europe.

What Table 14.2 illustrates is that surface heating is a significant factor in overall global warming, and it is occurring in every major EU country, including those that border the Alps. In fact the average temperature rise over all five of the main alpine countries is 0.30 °C. It is perhaps no wonder then that the alpine glaciers have been retreating for over a century, while those in Norway and New Zealand, where the population density (and also the economic activity) is much lower, have remained more stable. But what this warming is not due to is increased CO₂ levels in the atmosphere or an enhanced Greenhouse Effect. That is a completely separate issue.

The conclusion we can draw from this is that, in most developed countries, warming of up to 1.0 °C has occurred since pre-industrial times, and this warming is solely a result of industrial activity and the heat that is generated as a result of that activity. This will occur irrespective of the energy type or source used because it is the heat that is directly warming the planet, not increases in the concentration of waste gases that then add to the Greenhouse Effect. This also means that when the energy usage goes down, the temperature should go down.

This has major implications for future energy policy because it means that nuclear power and most renewables are no better than fossil fuels. It also means that the efficiency of energy generation is as important as the quantity of energy generation in determining the amount of warming.

Fig. 14.5: Efficiencies of different power sources.

As an example of the impact of energy efficiency consider the case of solar photovoltaics. The relative efficiencies of different power sources are illustrated in Fig. 14.5 above. Of these photovoltaics are among the least efficient. They are in fact only about 15% efficient, meaning that for every 100 joules of energy they harvest from the Sun, they only create 15 joules of electricity. Yet in order to do this solar cells need to be 95% efficient in terms of absorbing incoming solar radiation. In other words their albedo needs to be less than 0.05. That means that for every 100 joules of solar radiation that falls on a solar cell, 5 joules is reflected back into space, 15 joules is turned into electricity (which will then become surface heat at the point of use), and 80 joules becomes waste surface heat in the solar cell.

Now a fashionable policy proposal at the moment is to put large numbers of photovoltaics in the Sahara Desert and then pump the electricity they produce to wherever it is needed. The problem is that not only will the electricity generated heat the location of its end user, but the solar cells will heat up the desert by decreasing the local albedo from 0.40 to 0.05. That is a double whammy. It is global warming without the need for CO₂. Now you don't hear much about that from climate scientists.